For years, the power of AI has been offered up as a solution to influencer marketing challenges. But existing AI approaches have fallen short of expectations and have never delivered on a marketers’ ultimate objective: measuring the impact on the bottom line.
But that’s about to change. Here are three ways that AI will actually transform what brands can expect from their influencer marketing investments.
Measuring results rather than reach
Deep learning algorithms—the latest developments in AI—offer something that the technology has never been able to accomplish before: predicting campaign results. No longer will marketers need to settle for reach as their outcome. Instead, they will be able to optimize their campaigns on lower funnel metrics like sales.
Branded Entertainment Network (BEN) has built deep learning algorithms that offer predictive analytics specifically designed to increase the ROI of influencer marketing campaigns. It uses unstructured data—the video and audio data contained within the content itself—and combines it with campaign performance data to predict campaign results.
So far, the results have been impressive. Streaming television provider Philo has improved the clickthrough rate of its influencer campaigns by 172 percent and has increased its subscription conversion efficiency through influencer marketing by nearly 10X. This is a notable accomplishment for AI. Its predictive analytics aren’t simply increasing views or clicks, but actual sales.
Finding the right needles in a massive haystack
The number of viable influencers for brands to work with has blown up over the past year. According to Tubular, influencers drive 75 percent of all views on social media, and Klear reports that microinfluencers posted 84 percent of sponsored Instagram posts worldwide during 2018.
Given this explosion of content, one-off campaigns simply don’t work. Marketers need to work with multiple influencers for an extended period of time to have any impact. And while having reliable brand advocates can be helpful, the best strategy is to constantly add high growth influencers who can reach new, engaged audiences.
The challenge is finding them. Humans alone can’t possibly watch, analyze and predict performance across the hundreds of thousands of influencers posting content every day.
Deep learning, however, is not only able to analyze all of this content, but it is able to identify which influencers are a great match for a brand’s target audience, values or campaign objectives, and then predict which of those influencers will perform best. Marketers will be able to use influencer channels to reach their audiences quickly, efficiently and consistently over time.
Driving trust through transparency
Influencer fraud is certainly a problem. AI offers a solution that brings transparency to this issue. It can accurately uncover the percentage of bots within influencer accounts.
Yet bot percentages alone are only the first step in combatting influencer fraud. Through more sophisticated approaches like deep learning, AI will be able to provide marketers with granular insight into a channel’s health, growth and engagement. Combined with bot percentages, these metrics provide a more accurate prediction of a channel’s potential performance.
For many years, AI has offered marketers more hype than reality. It turns out that the solution to unlocking the power of AI for influencer marketing has been sitting right in front of us—within the content that gets watched every day. By tapping into the power of unstructured data, this will be the year deep learning brings something new to influencer marketing: the ability to optimize on performance.
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As the CEO of Branded Entertainment Network (BEN), a Bill Gates Company, Ricky Ray Butler is a passionate advocate of brand integration being the way forward in reaching audiences. He has been a pioneer in entertainment and influencer marketing for over 10 years.
